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H200624

Creative Destruction and Regional

Competitiveness

Niels Bosma Erik Stam Veronique Schutjens

Zoetermeer, December, 2006

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Creative Destruction and Regional Competitiveness

Niels Bosma1,4, Erik Stam1,2,3 and Veronique Schutjens1 ABSTRACT New firms stimulate competitiveness via market selection and competitive pressures, by forcing less efficient incumbents to exit or to improve their productivity. This way, both the creation and destruction of firms (turbulence) may improve competitiveness. In this paper the effect of turbulence on regional competitiveness (measured as total factor productivity and employment growth) is analysed in 40 regions in the Netherlands over the period 1988-2002. Our analyses suggest that turbulence leads to productivity growth in services but not so in manufacturing. Employment growth appears to benefit from firm dynamics in manufacturing. KEYWORDS: turbulence, entry and exit, productivity, employment, regional competitiveness JEL CODES: L10, M13, R11, O40 VERSION: 5 December 2006 1 Utrecht University, Department of Economic Geography 2 University of Cambridge 3 Max Planck Institute of Economics, Jena 4 EIM Business and Policy Research, Zoetermeer Corresponding author: Niels Bosma (n.bosma@geo.uu.nl) The usual disclaimer applies. The authors would like to thank Ron Boschma, Koen Frenken, Frank Neffke and André van Stel for comments on earlier drafts.

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INTRODUCTION

In the last decades, entrepreneurship has increasingly been linked with economic

growth (Wennekers and Thurik, 1999; Carree and Thurik, 2003). There is now a

widespread agreement that entrepreneurship is important for competitiveness (Porter

1990). In most studies that have investigated this presupposed link empirically,

entrepreneurship has been measured as new firm formation rates and regional

competitiveness as employment growth in regions (see e.g. Van Stel and Storey 2004;

Acs and Armington 2004). These studies in general equate entrepreneurship with new

firm formation and claim to be inspired by Schumpeter’s (1934; 1942) work on the

mechanisms of economic development, especially the role of entrepreneurship.

However, Schumpeter’s (1934) theory of “creative destruction” involves both creation

(new firm formation) and destruction (firm exit). This latter aspect reflects the

selection mechanism that is a crucial outcome of the process of competition and an

important driver of competitiveness and economic growth. In addition, not all new

firms contribute to progress in a regional economy, and although employment is an

important feature of economic development, competitiveness might better be

measured with other indicators.

Regarding competitiveness, authors like Porter (1990; 1998) and Krugman (1991)

have made a plea for using productivity as the indicator of competitiveness. A rising

standard of living in the long run depends on the productivity with which a nation’s

resources are employed (cf. O’Mahoney and Van Ark 2003). An important empirical

drawback of this indicator is that there is hardly any data available at the sub-national

scale (Kitson et al. 2004), and from other industries than manufacturing (Bartelsman

and Doms 2000). Another possible drawback is that it might reveal perverse effects,

when labour shedding (e.g. with an extensive shakeout of workers and closure of

plants) is the cause of improved (labour) productivity. Ideally, both employment

growth and productivity growth should go together (Kitson et al. 2004): increasing

productivity causes an improved competitive position, which leads to higher demands

of the goods and services produced, which in turn leads to an increased demand for

labour inputs. The question of how entrepreneurship affects (regional) productivity

growth is still unanswered (Acs and Armington 2004, p. 925).

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An important mechanism to stimulate regional competitiveness is competition among

economic actors. Competition can be enforced by new entrants, but only if these new

entrants push less efficient or less effective firms out of the market or when they

provide a threat to incumbents, which subsequently improve their efficiency. The

driving force of competitiveness is cost reduction through productivity improvements,

which ensures that on the long run, the “fit” firms prosper and the “unfit” firms do

not. Competition is therefore a central mechanism in economic evolution seen as the

progressive selection of more and more efficient techniques embodied in new and

existing firms (cf. Geroski 1989; Gowdy 1992). This competition will lead to

improved total factor productivity (TFP), but not necessarily to higher employment

levels. However, if new entrants are less efficient than incumbents, the efforts

involved in the emergence of these entrants waste valuable resources. In the latter

situation entrepreneurship – measured as new firm formation – is not a driver of

competitiveness at all. This situation has been identified in the literature as a

revolving door regime: entrants that have to exit relatively soon after start-up due to

an insufficient level of efficiency (Audretsch and Fritsch 2002). This revolving door

regime reflects a situation with high entry rates, but with no subsequent improvement

of either employment levels or productivity.

A more structural view of economic change provides a different role of

entrepreneurship. New entrants cause structural change when they introduce

innovations that create completely new knowledge (Metcalfe 2002) and possibly new

markets. This kind of entry does not necessarily drive out incumbents, but might do so

when the new markets substitute existing markets (e.g. the personal computers driving

out typewriters, and digital cameras driving out analogue film cameras). The former

situation might be called creative construction (Audretsch et al. 2006), in contrast to

the latter, which reflects creative destruction. This structural change might improve

both TFP and employment if the newly created market does not cannibalise existing

markets.

INSERT TABLE 1 ABOUT HERE

Table one summarises the three mechanisms discussed above. In this paper we will

analyse the effects of firm entry and exit on regional competitiveness. The key

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question of this study is: to what extent do firm entry and exit affect the

competitiveness of regions? We contribute to the existing literature by investigating

both TFP and employment growth as measures of competitiveness. We also make a

distinction along manufacturing and services. By separating these sectors we allow for

different implications as regards the relation between creative destruction and regional

competitiveness. Furthermore, we account for various determinants (control variables)

and in particular the business cycle effect on competitiveness.

ENTREPRENEURSHIP AND REGIONAL COMPETITIVENESS

Measuring regional competitiveness

The past decade has seen an emerging set of empirical studies relating entry (and to a

lesser extent, exit) to competitiveness. Competitiveness is often measured with either

employment growth or growth in total factor productivity (TFP). There are some

notable differences between these measures of growth. For example, during

recessions the efficiency measures by managers in incumbent firms might lead to

employment loss and TFP growth on the short term. On the medium term,

unemployment push entrepreneurship might absorb the employment loss, and

decrease TFP. In our model explaining TFP growth, rates of firm dynamics are

hypothesized to be a factor influencing regional growth additional to labour and

capital. Basically, we investigate whether firm entry and exit, apart from growth in

labour and capital stock (whether induced by existing or by new firms), invoke a

certain degree of economic growth. This approach relates to the one applied by Acs et

al. (2005). In this approach it should be acknowledged that entry of firms involves

labour and capital input. Therefore equating entrepreneurship to entry of firms (or

self-employment rates) may produce interdependencies between the input measures.

Exit, however, involves capital and labour losses. Therefore, turbulence rates may be

seen as the best measures for entrepreneurship dynamics to be included in the model

explaining TFP growth. Although we focus on total factor productivity as an

economic performance measure we do acknowledge that for economic development

in general, employment growth is highly important; increased productivity caused by

reductions in employment can have a negative short-run effect in the form of

unemployment. Kitson et al. (2004) argue that, if regional competitiveness can be

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measured at all, it should be determined by productivity, employment and the degree

of social welfare1.

Entry-exit: competitive selection

In the literature on productivity and economic growth two mechanisms are often

distinguished: productivity growth from market selection or “passive learning” which

leads to reallocation of output across firms, and productivity growth resulting from

“active learning” which contributes to productivity improvements within firms (and

possibly a shift of market shares). According to Jovanovic’ (1982) model of passive

learning and noisy selection, new firms are started by entrepreneurs that do not know

the (future) efficiency of their firm. In the post-entry period, entrepreneurs learn about

the efficiency of their firm by receiving signals from the market, i.e. whether their

firm is profitable or not (cf. Alchian 1950). These signals will be used by the

entrepreneur to decide on the continuation or exit of the firm. The efficient

(profitable) entrants will survive, while the inefficient ones will be pushed out of the

market. If more efficient firms substitute less efficient firms, this will improve the

productivity of the economy. This will happen at a faster speed when relatively many

new firms enter which force less efficient incumbents to exit: i.e. a high level of

turbulence.

In addition to passive learning, Ericson and Pakes (1995) also consider active learning

by firms. By nature, firms invest in R&D in order to raise their productivity. New

entrants might trigger incumbents to actively improve their productivity even further

in order to survive. Carlin et al. (2001) refer to this effect of competition on

productivity as ‘incentives’: encouraging improvements in technology, organisation

and effort on the part of existing establishments and firms. Pakes and Ericson (1998)

found that the empirical value of these models is contingent on the industry context:

post-entry performance in manufacturing was better explained by active learning,

while post-entry performance in retail confirmed the passive learning model, possibly

reflecting the relatively low entry and exit barriers in the latter industry. In the active

learning model, turbulence is not a necessary condition for productivity improvement.

More important is (the threat of) the supply of new, relatively more efficient, entrants.

1 Davidsson et al. 1994 found some evidence of the impact of regional start-up rates on an indicator for economic well-being, in Sweden.

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Competitive selection may affect productivity and employment levels. Several studies

have shown that entry and exit contributes to aggregate productivity growth, or total

factor productivity (TFP) in manufacturing (Bailey et al. 1992; Liu 1993; Carlin et al.

2001; Callejon and Segarra 1999). However, Caves (1998, p.1973) concludes in his

review that the effect of turbulence on subsequent productivity growth is marginal on

the short run, but that this effect improves on the longer run. Geroski (1989) found

that higher entry rates lead to higher productivity growth. He explains this by

assuming that entry stimulates competition, and greater competition spurs

productivity growth. Innovation turned out to be an even more important driver of

productivity (Geroski 1989; cf. Baily and Chakrabarti 1985). Until now, almost all

studies on the effect of firm dynamics on TFP concern manufacturing; studies in other

industry contexts have hardly been executed due to lacking (productivity) data.

Therefore, examining both TFP growth and employment growth, enables to make

inferences of firm dynamics and its impact on TFP growth, while keeping linked to

the existing set of empirical studies that generally focused on employment growth.

Ever since the asserted linkage of entrepreneurship with economic growth

(documented in e.g. Wennekers and Thurik, 1999; Carree and Thurik, 2003), there

have been multiple studies that emphasise the positive effect of entrepreneurship

(measured as new firm formation) on employment rates (Acs and Armington 2004;

Audretsch and Keilbach 2004; Reynolds 1999). Recently this effect of

entrepreneurship on employment growth has been refined with taking into account

variations in the spatial and temporal contexts in which this takes place (Audretsch

and Fritsch 2002; Fritsch and Mueller 2004; Van Stel and Storey 2004; Van Stel and

Suddle 2006). Fritsch (2006) concludes that the impact of entry rates on employment

growth in general takes an S-shape: direct positive returns in the first year are

followed by 2-5 years of negative returns in which the new firms have to improve

efficiency and the inefficient entries will exit the market. After this process, the long-

term effect turns out to be positive, i.e. regions with higher entry rates reflect higher

employment growth in the long run.

Studies on the effect of turbulence (thus including exit rates) on employment growth

show more mixed evidence: while Reynolds (1999) found a positive effect in the US,

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Audretsch and Fritsch (1996) and Fritsch (1997) found a negative effect of turbulence

in both the manufacturing and the service sectors in Germany. Audretsch and Fritsch

(1996) argued that innovative activity played a smaller role in Germany than in the

US, forcing less inefficient incumbents to exit more often in the latter than in the

former. In a more recent study, Audretsch and Fritsch (2002) show that the effect of

turbulence on employment depends on the type of region: a positive effect in regions

with an entrepreneurial regime, and a negative effect in regions with a revolving door

regime. In the latter context, new relatively inefficient firms enter and also exit within

a short period. This could be qualified as vicious turbulence, as there are (opportunity)

costs attached to such wasteful entrepreneurial efforts, and incumbents are not

triggered to improve either.

Innovative entry and regional growth

According to Eliasson (1996) there are four mechanisms of Schumpeterian creative

destruction leading to economic growth: 1) innovative entry that enforces (through

competition); 2) reorganization; 3) rationalization; 4) exit (shut down). The initial

mover of economic growth is entrepreneurship in the form of the entry of innovative

firms. The innovation that is introduced may be complementary to existing products,

and thus involve additional employment, but it may also be a (better) substitute of an

existing product and thus improve productivity. In the latter situation there will be

rivalry between the innovative entrant and incumbents, possibly forcing the

incumbents to reorganize and/or rationalize, or even to die (exit). Depending on the

newness of the innovation, incumbents can reorganize themselves in order to integrate

this innovation in their production process. When the innovation is too radical, the

incumbents will be forced to rationalize (contract) or even to shut down. The entry

process is therefore critical for economic growth, pushing the productivity of the

industry in the region upwards, by stimulating incumbents to improve their

productivity, and/or forcing inefficient firms unable to adapt to exit. This implicates

that entry may invoke improvements in regional productivity which are difficult to

capture when analysing firm-level data without information on the interlinkages

between firms2. Exit is important for not keeping ‘losers’ (inefficient or ineffective

2 In these kinds of studies at the firm-level, productivity of entrants is compared to that of incumbents and exiting firms. While these kinds of decomposition analysis are valuable, the impact of entry on performance of incumbents cannot be measured.

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firms) on for too long. It releases resources for more remunerable and more efficient

economic activity in other economic sectors.

DATA AND METHOD

Entry and exit at the regional level

In this paper we measure entry and exit, but also take into account turbulence (gross

turnover) as measures of entrepreneurship and selection. As regards measuring firm

dynamics, the industry under consideration is situated in a certain territorial context.

In general industrial economic studies take this for granted, implicitly assuming that

the national level is the most relevant territorial context. However, especially for

entry, competition and learning, the regional level might be more relevant than the

national level (Fritsch and Schmude, 2006).3 Considering economic growth,

globalization has also led to the belief that regional allocations of industrial excellence

(possibly transcending national borders) are overshadowing national economic

progress (Eliasson, 2003). In this study we specify regional firm dynamics (annual

numbers of entry and exit) relative to the stock of firms in the region.

Dataset

We have specified two sectors: manufacturing and services. The distinction between

these two major sectors is primarily data-driven, i.e. the availability of TFP and firm

dynamics data in the Netherlands. In our paper the manufacturing sector includes the

International Standard Industrial Classification code D, while the services sector

includes the ISIC codes J, K, N, O and P. We have used the most suitable level of

territorial aggregation for the Netherlands: the Corop-level of analysis (EU Nuts 3)

(cf. Van Stel and Nieuwenhuijsen 2004, Kleinknecht and Poot, 1992). The division in

40 Corop regions is based on regional commuting patterns that indicate regional

labour markets.

The regional panel dataset on annual entry and exit for the Netherlands in 40 regions

is available for a 14 year period (1988-2002). Registrations and deregistrations are

provided by the Dutch Chambers of Commerce. Entry includes independent new 3 Competition in product-markets, but especially in labour markets is likely to be concentrated in the home-region of the firm. Even more localized is probably the learning that takes place through knowledge spillovers (see Jaffe et al., 1993, Breschi and Lissoni 2003).

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businesses as well as new subsidiaries; exit includes bankruptcies as well as other

modes of firm exit. Unfortunately we cannot distinguish between exit due to business

closure (varying from simply finishing economic activity to forced liquidation) and

exit due to changes in ownership (i.e. mergers or acquisitions).

Figure 1 depicts turbulence and net entry rates of the 40 Dutch regions over time.

There appears to be a substantial variation between these firm dynamics measures

across regions, especially where turbulence is concerned (not pictured).4 Also, the

average turbulence rates during 1996-2004 are higher as compared to 1988-1995.

Apart from this long-term trend we also observe a business cycle pattern, notably in

the period 1996-2004. We thus experience regional and business cycle patterns, as

well as a general trend of increasing firm dynamics in the Netherlands.5 Since

business cycle effects are obviously also at play in our analysis of competitiveness

(see figure 2), we will account for business cycle effects on our regression model in

order to minimize the possible effects of spurious correlations.

Data on annual employment, value added and investment at the Corop level have been

taken from Statistics Netherlands and are available for the period 1988-2002.6 We

excluded five regions from the analysis in the manufacturing sector because their

regional growth rates are heavily determined by extraction (gas and electricity), which

could possibly interfere with our model. The capital stock has been calculated using

the Perpetual Inventory Method (PIM). Based on investments at sector and regional

level, an initial capital stock level was derived. The capital stock for every following

year has been calculated as the sum of the depreciated capital stock, plus investments

in the current year. The depreciation rates for both sectors have been estimated using

the initial levels of the capital stock in 1989 and investment levels from 1960-1976.

4 The F-statistics with respect to variance between regions amounts to 20.7, 13.0 and 5.9 for respectively turbulence, volatility and net entry in services. In manufacturing the corresponding F-values are 9.0, 10.7 and 2.3; all significantly different from zero (p<0.05). 5 See Bosma et al. 2005 for explanations of the trendwise increase in entrepreneurship for the Netherlands. There is one noteworthy issue as regards the economic slowdown of 1991-1993. This period was also characterized by intensive start-up stimulation by the Dutch government. Specifically, in 1993 there was an important relaxation of requirements to start new ventures (see Carree and Nijkamp, 2001). This relaxation, along with the increasing importance of the ICT sector with its low barriers to entry has probably overshadowed diminishing incentives to start a business from the business cycle’s point of view 6 Value added and investments have been corrected for inflation.

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Figure 2 demonstrates that the development in TFP differs from the development in

employment growth (employment is measured in full time equivalents and excludes

the self-employed). The difference is particularly striking in manufacturing in the

early 1990s, where employment growth is negative and TFP growth is positive: this is

a case of labour shedding in which a reduction of employment leads to a (short term)

increase in TFP. In services, TFP and employment also diverge in the early 1990s but

in a different way than in manufacturing: an increase in employment growth goes

hand in hand with a decrease in TFP. Overall, there is hardly any employment growth

in manufacturing, while TFP hardly increases in the service sector. The interregional

variance appears to be smaller for services, especially for TFP.7

FIGURE 2 ABOUT HERE

We will control for variables that are believed, in addition to firm dynamics, to impact

regional competitiveness. These are R&D intensity, population density and related

variety. A short note on our measure of related variety might be useful. Entropy

statistics have been used to measure sector variety (see Frenken et al. 2007). Related

variety measures the variety within each of two digit classes. The degree of

population density is measured by the percentage of people living in a highly

urbanized or urbanized area and supplied by Statistics Netherlands. It captures general

benefits of locating in dense regions. Related variety and population density are both

indicators of urbanization but to some extent their pattern differs over regions. Figure

3 displays related variety and population density for the 40 regions in the Netherlands.

Both indicators are time independent in our model. R&D intensity is taken from Van

Oort (2002) and is measured as the share of wages in innovative sectors with respect

to total wages, per region.

INSERT FIGURE 3 ABOUT HERE

Empirical Model

Following Geroski (1989) and Calléjon and Segarra (1999) we model firm dynamics

as a component of the total productivity in region i and year t, controlling for the

7 The interregional variance for TFP in services is weakly significant different from zero (p-value<0.10), while this variance for employment growth is significant with p<0.05. Both measures have a significant interregional variance for manufacturing (p<0.05).

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effects of labour and capital. For region i and year t, the quantity of output (value

added) itY is the result of the combination of capital and labour:

),,( itititit LKAFY = (1)

where output depends on the number of employees (L), the stock of physical capital

(K) and a ‘productivity index’ (A) that captures the variations in production that are

not attributable to changes in the use of labour and capital. Considering Hall’s

proposed general value added equation (Hall, 1986), the percent change of output

depends on three components. The first is the percent change in the productivity

index. The second is the product of the elasticity of scale and the percentage change in

capital. The third is the effect of market power, i.e. the percent change in the labour-

capital ratio weighted by the price-cost ratio (mark-upµ ) and the share of labour in

value added ( itα ).

itititititititit klkay εµαγ +−++= )dd(ddd , (2)

where the operator d reflects growth rates, expressed as first differences in logarithms.

By subtracting itititit kn d)1(d αα −+ on both sides we get an expression in which the

dependent variable is Solow’s residual:

itititititits lkka εαµγθ +−−++= )dd()1(1)d-(dit , (3)

Suppose that the growth of the corrected productivity index (da) can be modelled in

several components for region i and year t: percentage changes in industry

productivity which are constant over time and region (θ ) and improvements in

productivity resulting from firm dynamics (FD), regional R&D intensity (RD), the

degree of related variety in the region (RV) and population density (PD). We

minimise the danger of reversed causality by incorporating lagged effects of firm

dynamics on TFP growth. In accordance with a previous study (Bosma and

Nieuwenhuijsen, 2002) we model with lags of two years.8 This extension of equation

(3) leads to:

8 We expect the effect of firm dynamics on TFP to have a shorter lag in comparison with the effect on employment since monetary effects generally precede employment decisions. Baptista et al (2005) find a significant positive

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itititititiiitis lkkPDRVRDFD εαµγββββθθ +−−++++++= − )dd()1(1)d-(4322,1it (4)

We control for general business cycle effects (affecting all regions) by including

dummy variables representing every year of observation. Summarising, equation (4)

measures total factor productivity (TFP) growth or Solow’s residual for industry i and

region j as the sum of: (1) technical industrial progress in the strict sense ( iθ ), (2)

additional efficiency caused by firm dynamics (elasticity 1β ), regional intensity of

R&D expenditures (elasticity 2β ), the degree of related variety (elasticity 3β ) and

population density effects (elasticity 4β ), (3) economies of scale measured by γ , (6)

variations in the capital labour ratio weighted by the share of wages on value added,

the price cost ratio (mark-upµ ). We also explicitly model the possibility that benefits

of creative destruction in one region spills over to neighbouring regions. We control

for spatial autocorrelation by performing regression equation (4) in two rounds. In the

first round averages of the residuals in neighbouring regions are obtained. These enter

the regression in the second round, so that for each region some of the unexplained

variance in the neighbouring regions (in the first round) will be accounted for. To

prevent multicollinearity problems, we do not model entry and exit together in one

single model but use the combined measure of turbulence.

In our model explaining TFP growth, rates of firm dynamics are hypothesized to be a

factor influencing regional growth additional to labour and capital. Basically, we

investigate whether firm entry and exit, apart from growth in labour and capital stock

(whether induced by existing or by new firms), invoke a certain degree of economic

growth. The derived equation explaining employment growth rather than TFP growth

would be the following (assuming a standard Cobb-Douglas type function rather than

Hall’s equation, see Dekle 2002):

ititit

itiiitiit

itit

pkPDRVRDFDwl εα

ββββθαα

++−+++++−= − d1d)(1d1d 4324,1it (5a)

impact on employment using a lag of 4 years. Fritsch (2006) concludes that the impact of entry rates on employment growth in general takes an S-shape: direct positive returns in the first year are followed by 2-5 years of negative returns in which the new firms have to improve efficiency and the inefficient entries will exit the market. After this process, the long-term effect turns out to be positive, i.e. regions with higher entry rates reflect higher employment growth in the long run.

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Although the Cobb-Douglas model implies a negative elasticity of wage growth (dw),

the sign is arguably debated; generally the coefficient is estimated without specifying

α (see Storey and Van Stel 2004; Van Stel and Suddle 2006). We will do the same

by introducing coefficientλ . Since growth in regional prices ( ijdp ) is generally

unobservable, and in the case of the Netherlands changes in prices can be assumed not

to differentiate substantially across regions, the estimated equation becomes

ititiiitiit

it kPDRVRDFDwl εββββθα

λ +−+++++= − d)(1dd 4324,1it . (5b)

When the capital stock is also unavailable at the regional level the estimated equation

is the following:

itiiitiit

it PDRVRDFDwl εββββθα

λ ++++++= − )(1dd 4324,1it. (5c)

Note that, since 1<α , model (5a) predicts that the size of the estimated effects of firm

dynamics from (5c) will be larger as compared to equation 4 where TFP growth is the

dependent variable. We will use equation (5c) for making comparisons with the

existing studies investigating the effect of entry rates on growth in regional

employment (see e.g. Van Stel and Storey 2004; Van Stel and Suddle 2006).

RESULTS

Firm dynamics and TFP growth

Estimation results of equation (4) are depicted in table 2 and for manufacturing and in

table 3 for services. The first column in both tables (model I) presents the results of a

basic model excluding measures of creative destruction. The second model adds entry

rates, R&D and related variety and increases the performance of the model for

services but not so for manufacturing. Accordingly it is seen that there is no positive

effect of gross entry and turbulence on productivity growth for manufacturing (related

variety is significant), while there is a positive and significant effect for services (see

table 3). An explanation for this outcome is that entry in manufacturing is more

capital intensive and has a larger minimum efficient scale. Thus, barriers to entry are

higher in manufacturing. In terms of Nelson and Winter (1982), manufacturing can be

related to the routinized regime, whereas services can be related to the entrepreneurial

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regime.9 However, we are aware that the high level of sector aggregation applied in

the present empirical application could also interfere.

For services the designed spatial autocorrelation effect is, like in other empirical

studies (e.g. Van Stel and Storey, 2004; Fritsch and Mueller, 2004; Van Stel and

Suddle, 2006) significant. However, size and significance diminishes when we

include time dummies in the regression model III to account for business cycle

effects. This suggests that the designed spatial autocorrelation effect may

unintentionally pick up some temporal autocorrelation as a result of business cycles.

Therefore, one has to be cautious in interpreting the spatial autocorrelation as genuine

regional spillover effects10. Summarising, our results suggest that in services both

components of firm turbulence (i.e. entry and exit) positively influences TFP while

controlling for the effects of economies of scale, market power and business cycles

effects. Our analyses suggest that entrepreneurship (entry) and turbulence are

important drivers of productivity in the service sector, but not in manufacturing for

the Netherlands11.

INSERT TABLES 2+3 ABOUT HERE

Turbulence and employment growth

Table 3 and 4 show the impact of creative destruction on employment growth in

respectively manufacturing and services. Firm dynamics are lagged with 4 years.

Wage growth is also lagged and set to the wage growth between t=t-4 and t-2. Again

the results presented seem to underline the importance of controlling for business

cycle effects in manufacturing and services. Contrary to our results on TFP growth, it

is seen that firm gross entry and turbulence enhance employment growth in

manufacturing but not so in services. This is in accordance with Van Stel and Suddle

9 This highly connects to the two Schumpeter regimes (Schumpeter 1934, 1942) where manufacturing resembles Schumpeter II (routinized regime) and services resemble Schumpeter I (entrepreneurial regime), although the unit of analysis is quite different from Schumpeter’s ideas. 10 We have chosen to present a conservative measurement of business cycle effects in this paper; if we include time dummies, the coefficient measuring the designed spatial autocorrelation effect turns out to be insignificant in most regressions. Moreover, the size of the coefficient for entry decreases when year dummies are added, suggesting that not accounting for business cycle effects may result in overestimating the impact of firm dynamics on productivity. 11 See Braunerhjelm and Borgman (2004) for a similar finding on the effect of entrepreneurship on labour productivity.

17

(2006) who also find the largest effects in manufacturing using a similar model12.

Interestingly, exit does not appear to induce employment growth. These findings

support Eliason’s view that it is entry that triggers economic growth when measured

by employment growth. It should also be stressed that employment growth does not

include the number of self-employed, which may partly explain the lack of evidence

for positive effects of firm dynamics in services. The increasing trend of choosing for

self-employment status over employment-status while doing the same job has affected

services considerably in the Netherlands.

INSERT TABLES 4+5 ABOUT HERE

Summarizing, the analyses on TFP and employment growth show that entry and exit

positively affect regional competitiveness as measured by productivity in the services

sector, but not in manufacturing. In manufacturing the most spectacular improvements

in TFP revealed to go hand in hand with severe decline in employment, and thus

indicating socially unwanted labour shedding processes. In this sector, firm dynamics

has a positive impact on employment growth. The business cycle effect seems to be of

some importance in explaining regional competitiveness. It only slightly impacts the

effects of firm dynamics (especially in employment growth) but it seriously impacts

the designed effects for spatial autocorrelation.

CONCLUSION

This paper has investigated the effects of entry and exit on regional competitiveness.

The key question of this study was: To what extent do firm entry and exit affect the

competitiveness of regions? We found that especially in services firm entry has an

important effect on competitiveness as measured by TFP growth. In manufacturing,

the effect of firm dynamics on total factor productivity seems negligible in our sample

for the Netherlands, with the exception of the effect of entry and turbulence on

employment growth. In our study the signs of positive influence of firm turbulence on

economic growth appear to be highly contingent on the measures of competitiveness

12 The main differences being that employment growth is not measured in logarithm, entry rates are scaled on employment rather than the stock of businesses and estimations are based on non-overlapping periods of 3 years. Also, construction is estimated separately from manufacturing in their analysis.

18

and on the type of sector. In addition, we found that the business cycle affects

competitiveness more strongly than “creative destruction” in our analyses for the

Netherlands. We did not find evidence for the presence of negative effects of firm

turbulence in the Netherlands. So stimulating firm entry and not interfering in the

selection process seem to be reasonable policy measures in order to improve the

competitiveness of regions. Policy that is oriented towards saving firms from exit

might not be productive for stimulating regional competitiveness.

We believe this paper’s effort leads to some challenging areas of future research. Due

to data limitations there are some obvious drawbacks to our study. Apart from

replicating the study to other geographic areas, improvements can be made in the

following directions: (i) studying the effects of direct measures of innovative entry

versus those of non-innovative entry, (ii) measuring the effect of improved

incumbents on competitiveness and (iii) similar analysis of more disaggregated

industries.

We could not directly trace whether productivity growth is helped with the entry of

new efficient firms and the exit of old less inefficient ones. This would require

microdata (Baldwin 2006; Haskel and Khawaja 2003) that makes it possible to track

firms over time and calculate their productivity. One can then separate productivity

growth into two effects. The first is the part of productivity growth due to productivity

growth in incumbents that remain throughout the period. The second is the part of

productivity growth due to the entry of new firms and closure of old ones. If entrants

are above average productivity this raises productivity growth, and of course if firms

that exit are below average productivity this also raises productivity growth. The

micro-level approach applied in e.g. Baldwin et al. (2006) might therefore lead to

additional valuable information. However the micro-level approach does not measure

the impact new entrants have on the performance of incumbents. On aggregate a

sizable wave of new entrants could, in their first years of existence, not be very

efficient in terms of their own productivity but it could put pressure on the incumbents

to keep improving on productivity. The regional approach adopted in this paper does

acknowledge this possible effect – however without being able to specify its size. To

estimate these effects is perhaps the most interesting challenge for future research in

this area.

19

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23

Figure 1 Turbulence and net entry rates over time, averages over Dutch regions,

1988-2004 Manufacturing (35 regions) Services (40 regions)

0

24

68

10

1214

16

1988

1990

1992

1994

1996

1998

2000

2002

2004

turbulence net entry

0

24

6

8

1012

14

16

1988

1990

1992

1994

1996

1998

2000

2002

2004

turbulence net entry

Figure 2 TFP and employment growth over time, averages over Dutch regions, 1988-

2002 Manufacturing (35 regions) Services (40 regions)

-6

-4

-2

0

2

4

6

8

1988

1990

1992

1994

1996

1998

2000

2002

TFP growth Employment growth

-6

-4

-2

0

2

4

6

8

1988

1990

1992

1994

1996

1998

2000

2002

TFP growth Employment growth

24

Figure 3 Related variety by Corop regions in the Netherlands, in quartiles

Population density by Corop regions in the Netherlands, in quartiles

Table 1. Proposed effects of entry and exit on employment growth and productivity growth

Nature of entrants and exits Effect on competitiveness Employment

growth Productivity

growth Creative destruction / entrepreneurial regime

Innovative entrants

Inert obsolete incumbents

0 +

Revolving door Non-innovative entrants

Inefficient recent entrants

0 0

Creative construction Innovative entrants

0 + +

Table 2. Regression results for TFP growth in manufacturing.

I II III IV V Constant 0.04 ** -0.04 -0.01 -0.01 -0.03 TFP (t-1) -0.11 ** -0.14 ** -0.15 ** -0.15 ** -0.16 ** Entry (t-2) 0.04 -0.02 Turbulence (t-2) -0.04 Exit (t) 0.04 R&D -0.08 -0.07 -0.08 -0.08 Related variety 0.09 ** 0.07 ** 0.07 ** 0.09 ** Population density -0.01 -0.00 -0.00 -0.00 Economies of scale (γ) 0.69 ** 0.70 ** 0.79 * 0.79 * 0.83 Degree of market power (µ) 1.20 1.16 1.44 ** 1.45 ** 1.48 ** Spatial auto-correlation 0.28 ** 0.29 ** -0.02 -0.02 0.05 Year dummies yes yes yes Number of obs. 459 459 459 459 459 F statistic 9.80 5.99 5.55 5.56 6.53 Adj. R2 0.08 0.08 0.16 0.16 0.18

• p< .10, ** p< .05; for γ and µ the null hypothesis is γ=1 and µ=1

25

Table 3. Regression results for TFP growth in services. I II III IV V

Constant 0.03 ** 0.00 -0.02 ** -0.02 ** -0.01 TFP (t-1) -0.10 ** -0.11 ** -0.09 ** -0.09 ** -0.09 ** Entry (t-2) 0.17 ** 0.20 ** Turbulence (t-2) 0.14 ** Exit (t) 0.25 ** R&D -0.01 0.01 0.01 0.01 Related variety 0.02 0.02 * 0.02 ** 0.03 ** Population density -0.01 ** -0.01 ** -0.01 ** -0.01 * Economies of scale (γ) 0.53 ** 0.49 ** 0.53 ** 0.53 ** 0.55 ** Degree of market power (µ) 1.29 ** 1.29 ** 1.29 ** 1.29 ** 1.29 ** Spatial auto-correlation 0.62 ** 0.65 ** -0.06 -0.07 -0.10 Year dummies yes yes Yes Number of obs. 459 459 459 459 459 F statistic 107.9 59.0 36.1 35.8 35.8 Adj. R2 0.48 0.50 0.59 0.59 0.59

• p< .10, ** p< .05; for γ and µ the null hypothesis is γ=1 and µ=1

Table 4 Regression results for employment growth in manufacturing. I III III IV V

Constant -0.01 ** -0.07 ** -0.05 ** -0.04 ** -0.04 ** Empl growth (t-1) 0.17 ** 0.11 ** 0.05 0.07 0.09 * Entry (t-4) 0.32 ** 0.30 ** Turbulence (t-4) 0.09 ** Exit (t) -0.01 R&D 0.03 0.02 0.01 -0.01 Related variety 0.05 ** 0.05 ** 0.06 ** 0.06 ** Population density -0.04 ** -0.04 ** -0.04 ** -0.03 ** Change in wage rate, average (t-4/t-2) 0.05 0.32 0.34 0.36 0.12

Spatial auto-correlation 0.57 ** 0.57 ** 0.07 0.05 0.07 Year dummies yes yes Yes Number of obs. 389 389 389 389 389 F statistic 29.13 16.13 11.63 11.31 10.67 Adj. R2 0.18 0.21 0.30 0.30 0.28

* p< .10, ** p< .05

26

Table 5 Regression results for employment growth in services.

I III III IV V Constant 0.03 ** -0.02 -0.01 -0.00 -0.01 Empl growth (t-1) 0.08 * 0.08 * -0.07 -0.07 -0.07 Entry (t-4) -0.20 ** 0.01 Turbulence (t-4) 0.02 Exit (t) 0.15 R&D 0.11 ** 0.10 ** 0.10 ** 0.10 ** Related variety 0.07 ** 0.06 ** 0.06 ** 0.09 ** Population density -0.02 ** -0.02 ** -0.02 ** -0.02 ** Change in wage rate, average (t-4/t-2) -0.26 -0.31 0.31 0.32 0.37

Spatial auto-correlation 0.67 ** 0.63 ** 0.27 ** 0.26 ** 0.26 ** Year dummies yes yes Yes Number of obs. 389 389 389 389 389 F statistic 41.51 17.57 12.49 12.50 12.35 Adj. R2 0.24 0.24 0.32 0.32 0.33

* p< .10, ** p< .05

27

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